Neural translator

ABSTRACT

A method and apparatus are provided for processing a set of communicated signals associated with a set of muscles, such as the muscles near the larynx of the person, or any other muscles the person use to achieve a desired response. The method includes the steps of attaching a single integrated sensor, for example, near the throat of the person proximate to the larynx and detecting an electrical signal through the sensor. The method further includes the steps of extracting features from the detected electrical signal and continuously transforming them into speech sounds without the need for further modulation. The method also includes comparing the extracted features to a set of prototype features and selecting a prototype feature of the set of prototype features providing a smallest relative difference.

RELATED APPLICATIONS

This application claims priority to, and is a continuation of,co-pending U.S. application Ser. No. 13/560,675 having a filing date ofJul. 27, 2012, which is incorporated herein by reference, and whichclaims priority to U.S. application Ser. No. 11/825,785 (now U.S. Pat.No. 8,251,924) having a filing date of Jul. 9, 2007, which isincorporated herein by reference, and which claims priority toprovisional Patent Application No. 60/819,050, filed on Jul. 7, 2006,which is also incorporated herein by reference in its entirety.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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MICROFICHE/COPYRIGHT REFERENCE

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FIELD OF THE INVENTION

The field of the invention relates to the detection of nerve signals ina person and more particularly to interpretation of those signals.

BACKGROUND OF THE INVENTION

Neurological diseases contribute to 40% of the nation's disabledpopulation. Spinal Cord Injury (SCI) affects over 450,000 individuals,with 30 new occurrences each day. 47% of Spinal Cord Injuries causedamage above the C-4 level vertebra of the spinal cord resulting inquadriplegia, like former actor Christopher Reeve. Without the use ofthe major appendages, patients are typically restricted to assistivemovement and communication. Unfortunately, much of the assistivecommunication technology available to these people is unnatural orrequires extensive training to use well.

Amyotrophic Lateral Sclerosis (ALS) afflicts over 30,000 people in theUnited States. With 5,000 new cases each year, the disease strikeswithout a clearly associated risk factor and little correlation togenetic inheritance. ALS inhibits the control of voluntary musclemovement by destroying motor neurons located throughout the central andperipheral nervous system. The gradual degeneration of motor neuronsrenders the patient unable to initiate movement of the primaryextremities, including the arms, neck, and vocal cords. Acclaimedastrophysicist, Stephen Hawking has advanced stages of ALS. Hismonumental theories of the universe would be confined to his mind if hehadn't retained control of one finger, his only means of communicating.However, most patients lose all motor control. Despite these detrimentalneuronal effects, the intellectual functionality of memory, thought, andfeeling remain intact, but the patient no longer has appropriate meansof communication.

Amyotrophic Lateral Sclerosis and Spinal Cord Injury (ALS/SCI) are twoof the most prevalent and devastating neurological diseases. Many otherdiseases have similar detrimental effects: Cerebral Palsy, Aphasia,Multiple Sclerosis, Apraxia, Huntington's Disease, and Traumatic BrainInjury together afflict over 9 million people in the United States.Although the symptoms vary, the life challenges created by thesediseases are comparable to SCII ALS. With loss of motor control, theproduction of speech can also be disabled in severe cases. Without theuse of speech and mobility, neurological disease greatly diminishesquality of life, confining the patient's ideas to his or her own body.Although these individuals lack the capacity to control the airflowneeded for audible sound, the use of the vocal cords can remain intact.This creates the opportunity for an interface that can bypass thecommunicative barriers imposed by the physical disability.

In general, three subsystems are needed to produce audible speech from aconstant airflow. First, information from the brain innervates theperson's diaphragm, blowing a steady air stream through the lungs. Thisairflow is then modulated by the opening and closing of the secondsubsystem, the larynx, through minute muscle movements. The thirdsubsystem includes the mouth, lips, tongue, and nasal cavity throughwhich the modulated airflow resonates.

The process of producing audible speech requires all components,including the diaphragm, lungs and mouth cavity to be fully functionalto produce audible speech. However, inaudible′ speech, which is notmouthed, is also possible using these subsystems. During silent reading,the brain selectively inhibits the full production process of speech,but still sends neurological information to the area of the larynx.Silent reading does not require regulated airflow to generate speechbecause it does not produce audible sound. However, the secondsubsystem, the larynx, can remain active.

The muscles involved in speech production can stretch or contract thevocal folds, which changes the pitch of speech and is known asphonation. The larynx receives information from the cerebral cortex ofthe brain (labeled “1” in FIG. 1) via the Superior Laryngeal Nerve (SLN)(labeled “2” in FIG. 1). The SLN controls distinct motor units of theCricothyroid Muscle (CT) (labeled “3” in FIG. 1) allowing the muscle tocontract or expand. Each motor unit controls approximately 20 musclefibers which act in unison to produce the muscle movement of the larynx.Other activities involved in speech production include movement of themouth, jaw and tongue and are controlled in a similar fashion.

The complex modulation of airflow needed to produce speech depends onthe contributions of each one of these subsystems. Neurological diseasesinhibit the speech 2 production process, as the loss of functionality ofa single Component can render a patient unable to speak. Typically, anaffected patient lacks the muscular force needed to initiate a steadyflow of air. Previous technologies attempted to address this issue byemulating the activity of the dysfunctional speech productioncomponents, through devices such an electrolarynx or other voiceactuator technologies. However, they still require further complexmodulation capabilities which many people are no longer capable of. Forexample, a person both unable to initiate a steady flow of air andlacking proper tongue control would find themselves unable tocommunicate intelligibly using these other technologies. Despite thiscommunicative barrier, it is possible to utilize the functionality ofthe remaining speech subsystems in a neural assistive communicationtechnology. This novel technology can be utilized in a number of otheruseful applications, relevant to people both with and withoutdisabilities.

BRIEF SUMMARY OF THE INVENTION

A method and apparatus are provided for processing a set of communicatedsignals associated with a set of muscles of a person, such as themuscles near the larynx of the person, or any other muscles the personuse to achieve a desired response. The method includes the steps ofattaching a single integrated sensor, for example, near the throat ofthe person proximate to the larynx and detecting an electrical signalthrough the sensor. The method further includes the steps of extractingfeatures from the detected electrical signal and continuouslytransforming them into speech sounds without the need for furthermodulation. The method also includes comparing the extracted features toa set of prototype features and selecting a prototype feature of the setof prototype features providing a smallest relative difference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a system for processing neural signals shown generally inaccordance with an illustrated embodiment of the invention;

FIG. 2 depicts a sample integrated sensor of the system of FIG. 1;

FIG. 3 depicts a sample processor of the system of FIG. 1;

FIG. 4 depicts another sample processor of the system of FIG. 1 under analternate embodiment;

FIG. 5 is a feature vector library that may be used with the system ofFIG. 1;

FIG. 6 is a diagram illustrating another example of the system of FIGS.1 and 2 adapted to control a mobility device; and

FIG. 7 is a diagram illustrating another example of the system of FIGS.1 and 2 adapted to produce speech.

DETAILED DESCRIPTION OF AN ILLUSTRATED EMBODIMENT

FIG. 1 depicts a neural translation system 10 shown generally inaccordance with an illustrated embodiment of the invention. The neuraltranslation system 10 may be used for processing a set of communicatedsignals associated with a set of muscles near the larynx of a person 16.

The system 10 may be used in any number of situations where translationof signals between the person and the outside world is needed. Forexample, the system 10 may be used to provide a human-computer interfacefor communication without the need of physical motor control or speechproduction. Using the system 10, unpronounced speech (thoughts which areintended to be vocalized, but not actually spoken) can be translatedfrom intercepted neurological signals. By interfacing near the source ofvocal production, the system 10 has the potential to restorecommunication for people with speaking disabilities.

Under one illustrated embodiment, the system 10 may include atransmitting device 12 resting over the vocal cords capable oftransmitting neurological information from the brain. The integratedsensor is an integral one-piece device with its own signal detection,processing, and computation mechanism. Using signal processing andpattern recognition techniques, the information from the sensor 12 canbe processed to produce a desired response such as speech or commandsfor controlling other devices.

Under one illustrated embodiment, the system 10 may include a wirelessinterface where the transmitter 12 transmits a wireless signal 130 tothe processing unit 14. In other embodiments, the transmitters 12transmits through a hardwired connection 132 between the transmitter 12and data processing unit 14. The system 10 includes a single integratedsensor 12 attached to the neck of the person 16 and an associatedprocessing system 14. An example embodiment of the sensor 12 is shown inFIG. 2, and consists of three conductive electrodes 18,20,22: two ofthem (E1 and E2) may be of the same size and reference electrode (E3)may be slightly larger.

In one embodiment, the signal from Eland E2 first goes through a highpass filter 24 to remove low frequency (<10 Hz) bias, and is thendifferentially amplified using an instrumentation amplifier 26. Theinstrumentation amplifier also routes noise signals back into the userthrough the third electrode, E3.

In this embodiment, the resulting signal is further filtered for lowfrequency bias in a second high pass filter 28, and further amplifiedusing a standard operational amplifier 30. A microcontroller 32digitizes this signal into 14 bits using an analog to digital converter(ADG) 34 and splits the data point into. two 7-bit packets. Anadditional bit is added to distinguish an upper half from a lower halfof the data point (digitized sample), and each 8-bit packet istransmitted through a wired UART connection to the data processing unit14. Additionally, the microcontroller compares each sample with athreshold to detect vocal activity. The microcontroller also activatesand deactivates a feedback mechanism, such as a visual, tactile orauditory device 38. to indicate periods of activity to the user.

In this embodiment, the circuit board 36 of the sensor 12 is 13 mm wideby 15 mm long. E1, E2 are each 1 cm in diameter, and E3 is 2 cm indiameter. The size and spacing of the electrodes E1, E2, E3 has beenfound to be of significance. For example, to minimize discomfort to theuser, the sensor 12 should be as small as possible. However, the spacingof the electrodes E1, E2 has a significant impact upon the introductionof noise through the electrodes E1, E2. Under one illustrated embodimentof the invention, the optimal spacing of the electrodes E2, E2 is oneand one-half the diameter of the electrodes E1, E2. In other words,where the diameter of electrodes E1, E2 is 1 cm, the spacing is 1.5 cm.Additionally, the distance between the electrodes E1, E2, E3 and thecircuit board 36 has a significant impact on noise. The electrodes aredirectly attached to the circuit board 36, and all components (with theexception of the electrodes and LED) are contained in a metal housing tofurther shield it from noise. This sensor 12 has a small power switch40, and is attached to the user's neck using an adhesive 42 appliedaround the electrodes. Another embodiment could attach the sensor 12 tothe user's neck with a neckband.

Once the digital signal reaches the processing unit 14; it isreconstructed. Reconstruction is done within the reconstructionprocessor 44 in this embodiment by stripping the 7 data bits from each8-bit packet, and reassembling them in the correct order to form a14-bit data point. Once 256 data points arrive, the data is concatenatedinto a 256-point data window. Additional examples of the system areillustrated in FIG. 6 and FIG. 7. In FIG. 6, the data processing unit 14is adapted for control of wheelchair motors, and generates LeftWheelchair Motor signal 140 and Right Wheelchair Motor signal 142. InFIG. 7, the data processing unit 14 is adapted produce speech, andgenerates sound through Speaker 144.

In general, operation of the system 10 involves the detection of a setof communicated signals through the sensor 12 and the isolation ofrelevant features from those signals. As used herein, unless otherwisespecifically defined, the use of the word “set” means one or more. Forexample, a set of communicated signals comprises a single signal and maycomprise a plurality of signals. Relevant features can be detectedwithin the communicated signals because of the number of muscles in thelarynx and because different muscle groups can be activated in differentways to create different sounds. Under illustrated embodiments of theinvention, the muscles of the larynx can be used to generatecommunicated signals (and detectable features) in ways that couldgenerate speech or the speech sounds of a normal human voice. The mainrequirements are that the communicated signal detected by the sensor 12has a unique detectable feature, be semi-consistent and have apredetermined meaning to the user.

Relevant features are extracted from the signal, and these features areeither classified into a discrete category or directly transformed intoa speech waveform. Classified signals can be output as speech, orundergo further processing for alternative utilization (i.e., control ofdevices).

The most basic type of signal distinction is the differentiation betweenthe presence of a signal and the absence of a signal. This determinationis useful for restricting further processing, as it only needs to occurwhen a signal is present. There are many possible features that can beused for distinction between classes, but the one preferred is theenergy of a window (also known as the Euclidean vector length, ∥x∥,where x is the vector of points in a data window). If this value isabove a threshold, then activity is present and further processing cantake place. This feature, though simple and possibly suboptimal, has theimportant advantage of robustness to transient noise and signalartifacts.

After reconstruction of the signal, the processing unit computes the RMSvalue of each data window and temporally smoothes the resultingwaveform. This value is compared with a threshold value within athreshold function to determine the presence of activity. If activity ispresent, the threshold is triggered. A set of features is extractedwhich represents key aspects of the corresponding detected activity. Inthis regard, features may be extracted in order to evaluate theactivity. For example, the Fast-Fourier Transform (FFT) is a standardform of signal processing to determine the frequency content of asignal, One of the information bearing features of the FFT is thespectral envelope. This is an approximation of the overall shape of theFFT corresponding to the frequency components in the signal. Thisapproximation can be obtained in a number of ways including astatistical mean approximation and linear predictive coding.

In addition to the spectral envelope, action potential spikedistributions are another set of features that contain a significantamount of neurological information. An action potential is an electricalsignal that results from the firing of a neuron. When a number of actionpotentials fire to control a muscle, the result is a complex waveform.Using blind-deconvolution and other techniques, it is possible toapproximate the original individual motor unit action potentials (MUAPs)from the recorded activity. Features such as the distributions andamplitudes of the MUAP also contain additional information to helpclassify additional activity.

A number of features may be provided and evaluated using these methods,including frequency bands, wavelet coefficients, and attack and decayrates and other common time domain features. Due to the variability ofthe activity, it is often necessary to quantize the extracted features.Each feature can have a continuous range of values. However, forclassification purposes, it is beneficial to apply a discrete range ofvalues.

An objective metric may be necessary in order to compare features basedupon their ability to classify known signals. This allows selection ofthe best signal features using heuristics methods or automatic featuredetermination. Individual features can be compared, for example, byassuming a normal distribution for each known class.

$N_{1,2} = \frac{{\mu_{1} - \mu_{2}}}{\sigma_{1} - \sigma_{2}}$

The above index, N, (where “1” and “2” are two known signal classes,e.g. “yes” and “no”, and μ and σ may, respectively, be the mean andstandard deviation of a feature value) gives a simple measure of howdistinct a feature is for two known classes. In general, greater valuesof N indicate a feature with greater classifying potential. A similarmethod can be used for ranking the quality of entire feature vectors (aset of features), by replacing the means with the cluster centers andthe standard deviation with the cluster dispersion.

There are two distinct ways in which the extracted features may beprocessed. One way is to use closed-set classification, where responseclasses are associated with prototype feature vectors. For a givensignal, a response class is assigned by finding the prototype which bestmatches the signals' feature vector.

Once a set of feature values are assigned a particular meaning (class),these features can be saved as prototype features. Features can then beextracted from subsequent communicated real time signals and comparedwith the prototype features to determine a meaning and an action to betaken in response to a current set of communicated signals.

Machine learning techniques may be used to make the whole system 10robust and adaptable. Optimal prototype feature vectors can beautomatically adjusted for each user by maximizing the same exampleobjective function listed previously. Response classes and theirassociated prototype feature vectors can be learned either in asupervised setting (where the user creates a class and teaches thesystem to recognize it) or in an unsupervised setting (where classes arecreated by the system 10 and meaning is assigned by the user). Thisleads to three possibilities: the system performs learning to adapt tothe user, producing a complex but natural feature map relating theusers' signals to their intended responses; the user learns a predefinedfeature map using the real-time feedback to control a simple set offeatures; or, both the system and the user learn simultaneously. Thelatter is the preferred embodiment because it combines simplicity andnaturalness.

An adaptive quantization method may also be used to increase theaccuracy of the system 10 as the user becomes better at controlling thedevice. When first introduced to the device, an adaptive quantizationmethod may start the user with a low number of response classes thatcorresponds to large quantized feature step sizes. For example, a usercould start with two available response classes (“Yes”/“No”). A “Yes”response would correspond to a high feature value, while a “No” responsewould correspond to a low feature value. As the user develops theability to adequately control this feature between a high and low value,a new intermediate value may be introduced. With the addition of theintermediate value, the user would now have three available responseclasses (“Yes”/“No”/“Maybe”). Adaptively increasing the number ofquantized steps would allow the user to increase the usability of thedevice without the inaccuracy associated with first time use.

Discrete response classes of this sort have many potential applications,especially in the area of external control. Response classes can be tiedto everything from numerical digits or keyboard keys to the controls ofa wheelchair. In addition, they can be combined with hierarchicallystructured “menus” to make the number of possible responses nearlylimitless.

A second feature processing method is to use real-time feedback andcontinuous speech generation. Feedback is a real-time representation ofkey aspects of the acquired signal, and allows a user to quickly andaccurately adjust their activity as it is being produced. It allows thesystem to change dynamically based on its outputs. Various types ofstimuli such as visual, auditory, and tactile feedback help the userlearn to control the features present in their signal, thereby improvingthe overall accuracy of the distinguished responses. The most naturalmedium for real-time feedback is audible sound, where the signalfeatures are transformed into sound features.

Additional visual feedback allows the user to compare between responsesand adjust accordingly. Under one illustrated embodiment, when a userproduces a response, a series of colored boxes indicates the signalfeature “strength.” High and low signal strengths are indicated by awarm or cool color, respectively. One set of boxes may indicate thefeature signal strength of the duration and the energy of the lastresponse. Each time the user responds, the system 10 computes theoverall average of all of the user's responses. Another set of boxes maykeep track of these averages and gives the user an indication ofconsistency. Matching the colors of each set of boxes gives the user agoal which helps them gain control of the features. Once the featuresare mastered, the user can increase the number of responses and improveaccuracy. This is similar to how actual speech production is learnedusing audible feedback.

Control of the signal features allows control of the audible feedbackproduced, including the ability to produce any sound allowable by thetransformation. In the case of the continuous speech transformation, itis such that normal speech sounds are produced from the signal features.This approach bypasses many of the issues and problems associated withstandard speech recognition, since it is not necessary for the system torecognize the meaning of the activity. Rather, interpretation of thespeech sounds is performed by the listener. This also means that nofurther modulation is required for production of the speech sounds, allthat is required is the presence of laryngeal neural activity in theuser. As used herein, unless otherwise specifically defined,transforming the electrical signal “directly” into speech means that nofurther modulation of the speech sounds is required.

Listed below are two example approaches to the continuous generation ofspeech sounds from acquired activity. The first approach utilizesindependently controllable signal features to directly control the basicunderlying sound features that make up normal human speech. For example,the contraction levels of four laryngeal muscles can be extracted asfeatures from the acquired signal from the sensor 12 using a variationof independent component analysis. Each of these contraction levels,which are independently controllable by the user, are then matched to adifferent speech sound feature and scaled to the same range as thatspeech sound feature. Typical speech sound features include the pitch,amplitude and the position of formats. The only requirement is that anynormal speech sound should be reproducible from the speech soundfeatures. It is also of note that neither the source of the acquiredsignals nor the sound transformation needs to be that of a human, anycreature with similarly acquirable activity can be given the ability toproduce normal human speech sounds. Likewise, a human can be given thecapability of producing the sounds of another creature. As anotherexample, the independently controllable signal features can bedetermined heuristically by presenting the user with a range of signalfeatures and the user selecting the least correlated of those listed.

A second sample approach is to adaptively learn how to extract theactual speech sound features of the users' own voice. Audible words arefirst recorded along with their corresponding activity using the system10. The recorded speech is decomposed into its speech sound features,and a supervised learning algorithm (such as a multilayer perceptronnetwork) is trained to compute these values directly from the acquiredactivity. Once the transformation is suitably trained, it is no longernecessary to record audible speech. The learning algorithm directlydetermines the values of the speech sound features from the acquiredactivity, giving the user anew method of speaking. Once trained, theuser can speak as they naturally would and have the same speech soundsproduced.

These two example approaches are summarized as the human-learningapproach and the machine-learning approach. Both can be contrasted withdiscrete-based methods by noticing that there are no prototypes orsignal classes, instead the electrical signal and its features aredirectly transformed into speech sounds in real time to producecontinuous speech.

FIG. 3 depicts the signal processor 14 under an illustrated embodimentrelated to continuous speech. In FIG. 3, a feature extraction processor46 may extract features from the data stream when the data stream isabove the threshold value.—Features extracted by the feature extractionprocessor 46 may include one or more of the frequency bands, the waveletcoefficients and/or various common time domain features present withinthe speech related to activity near the larynx. The extracted featuresmay be transferred to a continuous speech transformation processor 48.Within the speech transformation processor 48, a speech featuresprocessor 50 may correlate and process the extracted features togenerate speech.

Under one illustrated embodiment, the extracted features are related toa set of voice characteristics. The set of voice characteristics may bedefined, as above, by the quantities of the pitch (e.g., the fundamentalfrequency of the pitch), the loudness (i.e., the amplitude of thesound), the breathiness (e.g., the voiced/unvoiced levels) and formats(e.g., the frequency envelope of the sound). For example, a firstextracted feature 52 is related to pitch, a second extractedcharacteristic 54 is related to loudness, a third characteristic 56 isrelated to breathiness and a fourth characteristic 58 is related toformats of speech. The set of features are scaled as appropriate.

To generate speech using. the above example speech features 53, thefundamental frequency is first used to generate sine waves 61 of thatfrequency and its harmonics. The sine waves are then scaled in amplitudeaccording to the loudness value 57, and the result is linearly combinedwith random values. in a ratio determined by the breathiness value 59.Finally, the resulting waveform is placed through a filter bankdescribed by the format value. The end result is speech generateddirectly from the sensor 12.

One embodiment could use this generated speech in a′ speech-to-textbased application 55, which would extract some basic meaning from thespeech sounds for further processing.

Under another alternate embodiment, the system 10. may be used forsilent communication through a electronic communication device (such asa cellular phone). In this case, the feature vectors may be correlatedto sound segments within files 64, 66 and provided as an audio input toa silent cell phone communication interface 70 either directly orthrough the use of a wireless protocol, such as Bluetooth or Zigbee. Theaudio from the other party of a cell phone call may be provided to theuser through a conventional speaker.

Under another embodiment, communication through a communication devicemay occur without the use of a speaker. In this case, the audio from theother party to the cell phone conversation is converted to an electricalsignal which is then applied to the larynx of the user.

In order to provide the electrical input to the larynx of the user, theaudio to the other party may be provided as an .input to the featureextraction processor 46 through a separate connection 77. A set offeature vectors 52, 54, 56, 58 is created as above. Matching of the setof feature vectors with a file 64, 66 may be made based upon thesmallest relative distance as discussed above to identify a set of wordsspoken by the other party to the conversation.

Once the file 64, 66 is identified for each data segment, the contentsof the file 64, 66 are retrieved. The contents of the file 64, 66 may bean electrical profile that would generate equivalent activity of thevocal aperture of the user. The electrical profile is provided as a setof inputs to drivers 70, 72 and, in tum, to the electrodes 18, 20 andlarynx of the person 16.

In effect, the electrical profile causes the user's larynx to contractas if the user were forming words. Since the user is familiar with thewords being formed, the effect is that of words being formed and wouldbe understood based upon the effect produced in the larynx of the user.In effect, the user would feel as if someone else were forming words inhis/her larynx.

In another illustrated embodiment, the output 72 of the system may be asilent form of speech recognition. In this case, the matched files 64,66 may be text segments that are concatenated as recognized text on theoutput 72 of the system 10.

In another illustrated embodiment, the output 74 of the system 10 may beused in conjunction with other sound detection equipment to cancelambient noise. In this case, the matched files 64, 66 identify speechsegments. Ambient noise (plus speech from the user) is detected by amicrophone which acquires an audible signal. The identified speechsegments are then subtracted from the ambient noise within a summer 73.The difference is pure ambient noise that is then output as an ambientnoise rejection signal 74, thereby eliminating unwanted ambient noisefrom speech.

In another illustrated embodiment, the output 76 may be used as aninter-species voice emulator. For example, it is known that someprimates (e.g., chimps) can learn sign language, but cannot speak like ahuman because the required structure is not present in the larynx of theprimate. However, since primates can be taught sign language, it is alsopossible that a primate could be taught to use their larynx tocommunicate in an audible, human sounding manner. In this case, thesystem 10 would function as an interspecies voice emulator.

FIG. 4 depicts a processing system 14 under another illustratedembodiment. In this case, the feature extraction processor 46 extracts aset of features from the data stream and sends the features to aclassifier processor 78. The classifier processor 78 processes thefeatures to identify a number of RMS peaks by magnitude and location.The data associated with the RMS peaks is extracted and used to classifythe extracted features into one or more feature vectors 80, 82, 84, 86associated with specific types of communicated signals (e.g., “GoForward”, “Stop”, “Go Left”, “Go Right”, etc.). The classificationprocessor 78, which compares each feature vector 80, 82, 84, 86 with aset of stored prototype feature vectors 64, 66 corresponding to the setof classifications and best possible responses. The best matchingresponse may be chosen by the comparison processor 60 based on aEuclidean or some other distance metric, and the selected file 64, 66 isused to provide a predetermined output 88, 90.

Under one illustrated embodiment of the invention (FIG. 5), one or morelibraries 118 of predetermined speech elements of the user 16 areprovided within the comparison processor 60 to recreate the normal voiceof the user. In this case, the normal speech of the user is captured andrecorded both as feature vectors 110, 112 and also as correspondingaudio samples 114, 116. The feature vectors 110, 11,2 may be collectedas discussed above. The audio samples 114, 116 may be collected througha microphone and converted into the appropriate digital format (e.g., aWA V file). The feature vector 110, 112 and respective audio sample 114,116 may be saved in a set of respective files 120, 122.

In this illustrated embodiment, the electrical signals detected throughthe sensor 12 from the user are converted into the normal speech in thevoice of the user by matching the feature vectors of the real timeelectrical signals with the prototype feature vectors 110, 112 of theappropriate library file 120, 122. Corresponding audio signals 114, 116are provided as outputs in response to matched feature vectors. Theaudio signal 114, 116 in the form of pre-recorded speech may be providedas an output to a speaker through a pre-recorded speech interface 92 oras an input to a cell phone or some other form of communication channel.

By using the system 10 in this manner, a user standing in a very noisyenvironment may engage in a telephone conversation without the noisefrom the environment interfering with the conversation through thecommunication channel. Alternatively, the user may simply form the wordsin his/her larynx without making any sound so as to privately engage ina cell phone conversation in a very public space.

In another illustrated embodiment, the library 118 contains audiblespeech segments 114, 116 in an idealized form. This can be of benefitfor a user who cannot speak in an intelligible manner. In this case, thefeature vectors 110, 112 are recorded and associated with the idealizedwords using the classification ‘process as discussed above. In cases ofsevere disability, the user may need to use the user display/keyboard 68to associate the recorded feature vectors 110, 112 with the idealizedspeech segments 114, 116. When a real time feature vector is matchedwith a recorded prototype feature vector, the idealized speech segmentsare provided as an output through an augment standard speech recognitionoutput 96.

In another illustrated embodiment, the library 118 contains motorcommands 114, 116 that are provided to a mobility vehicle (such as apowered wheelchair) through a mobility vehicle interface 94. This can beof benefit for a user who may be a quadriplegic or otherwise cannot movehis/her arms. In this case, the user may select wheelchair commands bysome unique communicated signal (e.g., activating his larynx to voice acommand string such as the words “wheelchair forward”). As above, thefeature vectors 110, 112 may be recorded from the user and associatedwith the commands using the classification process as discussed above.In cases of severe disability, the user may need to use the userdisplay/keyboard 68 to associate the recorded feature vectors 110, 112with the commands 114, 116. When a real time feature vector is matchedwith a recorded prototype feature vector, the corresponding wheelchairmotor command is provided as an output through the wheelchair interface94.

In another illustrated embodiment, the library 118 contains computermouse (cursor) and mouse switch commands 114, 116 through a computerinterface 98. This can be of benefit for a user who may be aquadriplegic or otherwise cannot move his/her arms. In this case, theuser may select computer command by some unique communicated signal(e.g., activating his larynx to otherwise voice a command string such asthe words “mouse click”). As above, the feature vectors 110, 112 may berecorded from the user and associated with the commands using theclassification process as discussed above. In cases of severedisability, the user may need to use the user display/keyboard 68 toassociate the recorded feature vectors 110, 112 with the commands 114,116. When a real time feature vector is matched with a recordedprototype feature vector, the corresponding computer command is providedas an output through a computer interface 98.

In another illustrated embodiment, the library 118 contains commands114, 116 for a communication device (such as a cellular phone). This canbe of benefit for a user who may be a quadriplegic or otherwise cannotmove his/her arms. In this case, the cell phone commands may be in theform of a keypad interface, or voice commands through a voice channel toa voice processor within the telephone infrastructure. The system 10 maybe provided with a plug in connection to a voice channel and/or to aprocessor of the cell phone through a cell phone interface 100. The usermay select cell phone commands by some unique communicated signal (e.g.,activating his larynx “to voice a command string such as the words“phone dial”). As above, the feature vectors 110, 112 may be recordedfrom the user and associated with the commands using the classificationprocess as discussed above. In cases of severe disability, the user mayneed to use the user display/keyboard 68 to associate the recordedfeature vectors 110, 112 with the cell phone commands 114, 116. When areal time feature vector is matched with a recorded prototype featurevector, the cell phone commands are provided as an output through thecell phone interface 100.

In another illustrated embodiment, the library 118 contains prostheticcommands 114, 116. This can be of benefit for a user who may be aquadriplegic or otherwise cannot move his/her arms. The user may selectprosthetic command by some unique communicated signal (e.g., activatinghis larynx to voice a command string such as the words “arm bend”). Asabove, the feature vectors 110, 112 may be recorded from the user andassociated with the commands using the classification process asdiscussed above. In cases of severe disability, the user may need to usethe user display/keyboard 68 to associate the recorded feature vectors110, 112 with the prosthetic commands 114, 116. Selected prostheticcommands may be provided to the prosthetic through a prostheticinterface 102.

In another illustrated embodiment, the library 118 contains translatedwords and phrases 114, 116. This can be of benefit for a user who needsto be able to converse in some other language through a languagetranslation interface 104. The user may select language translation bysome unique communicated signal (e.g., activating his larynx to voice acommand string such as the words “voice translation”). As above, thefeature vectors 110, 112 may be recorded from the user and associatedwith the translated words and phrases using the classification processas discussed above. When a real time prototype feature vector is matchedwith a recorded feature vector, the translated word or phrase isprovided as an output through language translation interface 104.

In another illustrated embodiment, the library 118 containsenvironmental (e.g., ambient lighting, air conditioning, etc.) controlcommands 114, 116. This can be of benefit for a user who needs to beable to control his environment through a environmental controlinterface 106. The user may select environmental control by some uniquecommunicated signal (e.g., activating his larynx to voice a .commandstring such as the words “temperature increase”). As above, the featurevectors 110, 112 may be recorded from the user and associated with theenvironment using the classification process as discussed above. Incases of severe disability, the user may need to use the userdisplay/keyboard 468 to associate the recorded feature vectors 110, 112with the environmental control commands 114, 116. When a real timefeature vector is matched with a recorded prototype feature vector, thecorresponding environmental control is provided as an output through theenvironmental control interface 106.

In another illustrated embodiment, the library 118 contains game consolecontrol commands 114, 116. This can be of benefit for a user who needsto be able to control a game console through a game console controller108. The user may select game console by some unique communicated signal(e.g., activating his larynx to voice a command string such as the words“Control pad up.”). As above, the feature vectors 110, 112 may berecorded from the user and associated with the game console controlcommands using the classification process as discussed above. In casesof severe disability, the user may need to use the display/keyboard 68to associate the recorded feature vectors 110, 112 with the game consolecontrol commands 114, 116. When a real time feature vector is matchedwith a recorded feature vector, the corresponding game control commandis provided as an output through the game control interface 108.

The reliability of the system 10 is greatly enhanced by use of theintegrated sensor 12, including the processor 14. The integration of thesensor 12 enables data collection to be performed much more efficientlyand reliably than was previously possible, and allows the data processor14 to operate on the signal of a single sensor where multiple sensorswere previously required in other devices. It also enables portabilityand mobility, two practical concerns which were not previouslyaddressed. The sensor 12 contains a mix of analog and digital circuitry,both of which are placed extremely close to the electrodes. Minimizingthe length of all analog wires and traces allows the sensor 12 to remainextremely small and sensitive while picking up a minimal amount ofexternal noise. A microcontroller digitizes this signal and prepares itfor reliable transmission over a longer distance, which can occurthrough a cable or a wireless connection. Further processing isapplication-dependent, but shares the general requirements outlinedabove. It should be noted that possible embodiments can have differentvalues, algorithms, and form factors from what was shown, while stillserving the same purpose.

The system 10 offers a number of advantages over prior systems. Forexample, the system 10 functions to provide augmentative communicationfor the disabled, using discrete classification (including phrase-basedrecognition) of activity from the user. The system 10 can accomplishthis objective by comparing signal features to a set of storedprototypes, to provide a limited form of communication made possible forthose who otherwise would have no way of communicating.

It should be specifically noted in this regard that the system 10 is notlimited to articulate words, syllables or phonemes, but may also beextended to activity that would otherwise produce unintelligible asspeech or even no sound at all. The only requirement in this case is’that the electrical signal detected through the sensor 12 need to bedistinguishable based upon some extractable feature incorporated intothe prototype feature vector.

The system 10 may also provide augmentative communication for the peoplewith disabilities, using continuous speech synthesis. By directlytransforming signal features into speech features, a virtually unlimitedform of communication is possible for those who otherwise would lack thesound modulation capabilities to prod{tilde over ( )}ce intelligiblespeech.

The system 10 can also provide silent communication using discreteclassification (including phrase-based recognition). By comparing asignal to a set of stored prototypes, a limited form of silentcommunication is possible.

The system 10 can also augment speech recognition. In this case,standard speech recognition techniques can be improved by the processingand classification techniques described above.

The system 10 can also provide a computer interface. In this case,signal processing can be done in such a way as to emulate and augmentstandard computer inputs, such as a mouse and/or keyboard.

The system 10 can also function as an electronic communication deviceinterface. In this case, signal processing can be done in such a way asto emulate and augment standard cell phone inputs, such as voicecommands and/or a keypad.

The system 10 can also provide noise reduction for communicationequipment. By discerning which sounds were made by the user and whichwere not, a form of noise rejection can be implemented.

The system 10 can provide inter-species voice emulation. One species canbe given the vocal capabilities of another species, enabling a possibleform of inter-species communication.

The system 10 can provide universal language translation. Signalprocessing can be integrated into a larger system to allow recognizedspeech to be transformed into another language.

The system 10 can provide bidirectional communication using musclestimulation. The process flow of the invention can be reversed such thatexternal electrical stimulation allows communication to a user.

The system 10 can provide mobility, for example, to people withdisabilities (including wheelchair control). By comparing signalfeatures to a set of stored command prototypes, a self-propelledmobility vehicle can be controlled by the invention.

The system 10 can provide prosthetics control. By comparing signalfeatures to a set of stored command prototypes, prosthetics can becontrolled by the invention.

The system 10 can provide environmental control. By comparing signalfeatures to a set of stored command prototypes, aspects of a users'environment can be controlled by the invention.

The system 10 can be used for video game control. Signal processing canbe done in such a way as to emulate and augment standard video gameinputs, including joysticks, controllers, gamepads, and keyboards.

A specific embodiment of the method and apparatus for processing neuralsignals has been described for the purpose of illustrating the manner inwhich the invention is made and used. It should be understood that theimplementation of other variations and modifications of the inventionand its various aspects will be apparent to one skilled in the art, andthat the invention is not limited by the specific embodiments described.Therefore, it is contemplated to cover the present invention and any andall modifications, variations, or equivalents that fall within the truespirit and scope of the basic underlying principles disclosed andclaimed herein.

1-15. (canceled)
 16. A method for communication, said method comprising:attaching a sensor near an area of a user's body associated with speechproduction; detecting an electrical signal from the user's nervoussystem through the sensor; and processing the detected electrical signalto provide information intended for communication by the user.
 17. Themethod of claim 16, wherein the processing comprises extracting a set offeatures of the detected electrical signal.
 18. The method of claim 17,comprising comparing the extracted set of features with a set ofprototype features.
 19. The method of claim 18, wherein the set ofprototype features corresponds to at least one of multiple responseclasses.
 20. The method of claim 19, wherein the multiple responseclasses comprise an affirmative response class and a negative responseclass.
 21. The method of claim 16, wherein the area of the user's bodyassociated with speech production is the user's brain.
 22. The method ofclaim 18, further comprising: selecting a prototype from the set ofprototype features with a minimum difference between the prototypefeature and the set of features of the signal.
 23. The method of claim22, further comprising: controlling a computing device using a commandassociated with the prototype.
 24. The method of claim 22, furthercomprising: controlling an electronic device using a command associatedwith the prototype.
 25. The method of claim 16, further comprising:wirelessly transmitting the electrical signal to a signal processingunit.
 26. An apparatus for communication comprising: a sensorconfigurable to be attached near an area of a user's body associatedwith speech production; an electrode configurable to detect anelectrical signal from the user's nervous system through the sensor; anda processor configurable to process the electrical signal to provideinformation intended for communication by the user.
 27. The apparatus ofclaim 26, wherein the processor comprises an extraction processorconfigurable to extract a set of features from the detected electricalsignal.
 28. The apparatus of claim 27, wherein the classificationprocessor is configurable to compare the extracted features with a setof prototype features, wherein the set of prototype features correspondsto at least one of multiple response classes.
 29. The apparatus of claim27, wherein the multiple response classes include an affirmativeresponse class and a negative response class.
 30. The apparatus of claim26, wherein the area of the user's body associated with speechproduction is the user's brain.
 31. The apparatus of claim 28, furthercomprising: a comparator configurable to select a prototype feature ofthe set of prototype features with the smallest relative difference. 32.The apparatus of claim 31, wherein a command associated with theprototype is configurable to control a computing device.
 33. Theapparatus of claim 31, wherein a command associated with the prototypeis configurable to control an electronic device.
 34. The apparatus ofclaim 26, further comprising: an analog to digital converter disposedwithin the sensor and configurable to convert the detected electricalsignal into an equivalent digital signal.
 35. The apparatus of claim 34,further comprising: a wireless transmitter configurable to wirelesslytransmit the digital signal to a signal processing unit.